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Remote Sens. 2017, 9(5), 456; doi:10.3390/rs9050456

Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach

School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
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Academic Editors: James Campbell and Prasad S. Thenkabail
Received: 26 February 2017 / Revised: 1 May 2017 / Accepted: 4 May 2017 / Published: 8 May 2017
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Abstract

Impervious surfaces have been widely recognized as an indicator for urbanization and environment monitoring. Plenty of methods have been proposed to extract impervious surfaces using remote sensing images. However, accurately extracting impervious surface is still a challenging task due to the confusion between impervious surface and bare soil. Thus, this paper presents a hybrid approach consisting of spectral-spatial feature sparse representation (SS-SR) and post-processing to extract urban impervious surface from hyperspectral images. We first extracted spectral and spatial features from hyperspectral images. Then, the spectral and spatial information of a pixel is represented by the vector stacking strategy. Each pixel vector can be represented by a linear combination of a few atoms from a learned dictionary, which is more suitable for impervious surface estimation. The sparse coefficients were automatically learned and then used for extracting impervious surface. The proposed impervious surface extraction method was evaluated with four hyperspectral datasets. We compared our algorithms with the state-of-the-art per-pixel based impervious surface extraction methods. The encouraging experimental results demonstrate the SS-SR algorithm generally outperforms the classic support vector machines and random forest. The improvement is more significant when combining SS-SR with post-classification approach. View Full-Text
Keywords: impervious surface; spectral-spatial feature; sparse representation; hyperspectral images impervious surface; spectral-spatial feature; sparse representation; hyperspectral images
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, S.; Gu, G. Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach. Remote Sens. 2017, 9, 456.

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